Data mining of massive datasets in healthcare

Managing the distribution of healthcare is seen to involve data collected a t every encounter of each patient with any provider, pharmacy, payer, or go vernment agency. These data and their analysis are massive on multiple dime nsions: of patient-encounter records; of variables (administrative, diagnos ti,c and procedural) and their derived indicators; of the related clinical knowledge resources; of the clinical and administrative issues to be addres sed; and of the diversity of the audience for the analysis. Statistical and computational strategies for massive analysis of these massive data includ e a principle of "layered recalibration" used to maintain statistical model s, and an emphasis on presentation, including report generation, and specia lized software systems. These ideas are implemented in the Performance iQ p roducts of QuadraMed Corp. developed by the author.